class_docstr: Default YOLO training transform which includes tons of image augmentations.
  
  Parameters
  ----------
  width : int
      Image width.
  height : int
      Image height.
  net : mxnet.gluon.HybridBlock, optional
      The yolo network.
  
      .. hint::
  
          If net is ``None``, the transformation will not generate training targets.
          Otherwise it will generate training targets to accelerate the training phase
          since we push some workload to CPU workers instead of GPUs.
  
  mean : array-like of size 3
      Mean pixel values to be subtracted from image tensor. Default is [0.485, 0.456, 0.406].
  std : array-like of size 3
      Standard deviation to be divided from image. Default is [0.229, 0.224, 0.225].
  iou_thresh : float
      IOU overlap threshold for maximum matching, default is 0.5.
  box_norm : array-like of size 4, default is (0.1, 0.1, 0.2, 0.2)
      Std value to be divided from encoded values.
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code: def _target_generator(self):
      if self._internal_target_generator is None:
          if self._net_none:
              return None
          from ....model_zoo.yolo.yolo_target import (
              YOLOV3PrefetchTargetGenerator,
          )
  
          self._internal_target_generator = (
              YOLOV3PrefetchTargetGenerator(
                  num_class=self._num_classes, **self._kwargs
              )
          )
          return self._internal_target_generator
      else:
          return self._internal_target_generator
  
file_path: gluon-cv/gluoncv/data/transforms/presets/yolo.py
imports: from __future__ import absolute_import
  import numpy
  import mxnet
  from mxnet import autograd
  from ..None import bbox
  from ..None import image
  from ..None import experimental
label_desc: Use image node_id 2 for calling the YOLO3DefaultTrainTransform obj's underlying member method code with example usage: obj._target_generator() and returns: self, self, None